Documenting Annual Differences in Vegetation Cover, Height ...€¦ · Michaela Clingaman, Ashley...

Documenting Annual Differences in Vegetation Cover, Height and Diversity near Barrow,

Alaska

Timothy Frederick Botting

A Thesis Submitted to the Graduate Faculty of

GRAND VALLEY STATE UNIVERSITY

In

Partial Fulfillment of the Requirements

For the Degree of

Master of Science

Biology

April 2015

Thesis Approval Form

@ GRAND V\LLEY STATE UNIVERSITY

The signatories of the committee below indicate that they have read and approved the thesis of Timothy Frederick Botting in partial fulfillment of the requirements for the degree of Master of Science.

D(f{/t,J?Thes~ .0~

mes Dunn, Committee member

«5l{~/ Dr. Gary Greer:coilllilitteeiY

Accepted and approved on behalf of the College of LiberaLArts and Sciences

_ of the Colleg

4-t)-)o t5 Date

Y-tr --ZPJr Date

1/t5 j UJZS Date

~A"'y/7~ s-., ld-/;;-r

Date Date

3

Acknowledgements

I thank my advisor Dr. Robert Hollister and committee members Drs. James Dunn, Gary Greer

and Heather Rueth for their help, guidance and support throughout my graduate studies. I also

thank the principle investigators Drs. Steve Oberbauer and Craig Tweedie for obtaining funding

and providing insights utilized in this study. I am indebted to current and former Grand Valley

State University Arctic Ecology Program members, University of Texas at El Paso researchers as

well as George Washington University researchers for providing data used in this study. I

specifically acknowledge Jeremy May, Robert Barrett, Kelseyann Kremers, Jennifer Liebig,

Michaela Clingaman, Ashley Brecken, Sergio Vargas, Ryan Cody and Kelsey Nyland. I thank

Polar Field Services, the Barrow Arctic Science Consortium and UMIAQ for logistical support.

Funding for this study was provided by the National Science Foundation and the Grand Valley

State University Presidential Research Grant.

4

Abstract

Vegetation in the Arctic has been shown to respond to climate change. Documented changes

have the potential to result in numerous ecosystem consequences. Therefore, understanding

vegetation change is of great importance. This study documents changes in tundra vegetation

with a focus on understanding the influence of annual differences in weather. Vegetation was

sampled using a point frame method on 98 1-m2 plots in 2010 and 2013 near Barrow, Alaska. A

subset of 30 of these plots was also sampled in 2012 and 2014. Plant encounters were identified

to species and grouped into one of the following functional groups: bryophytes, deciduous

shrubs, forbs, graminoids, lichens, litter or standing dead. Plant height and species diversity

metrics were also calculated. The following abiotic variables were used to quantify difference in

weather between years: air temperature, precipitation, thawing degree days, thaw depth, soil

temperature and soil moisture. For analysis, focus was on the 30 plots that were sampled over 4

years. The cover of all functional groups, the height of graminoids and alpha diversity changed

significantly; the magnitude of changes was large. Significant correlations occurred between the

following vegetation metrics and abiotic variables: the cover of forbs and soil temperature; the

cover of graminoids and all abiotic variables except soil moisture; the cover of litter and air

temperature, thawing degree days, soil temperature and soil moisture; the cover of standing dead

and all measured abiotic variables; and the height of graminoids and soil moisture. The strongest

correlations between abiotic variables and vegetation metrics varied between functional groups;

this suggests multiple abiotic factors should be considered when attempting to explain observed

changes. In summary, this study found large annual changes in vegetation cover, height and

diversity that were presumably due to variability in weather of each year. While a longer

consecutive time series is needed to better understand the relationship between the weather of a

5

given year and vegetation dynamics, caution should be used when interpreting the results of

long-term vegetation monitoring that is sampled over a coarse time series because of large

annual variability shown in this study.

6

Table of Contents

List of Tables .................................................................................................................................. 7 List of Figures ................................................................................................................................. 9

Introduction ................................................................................................................................... 12 Global Environmental Changes ................................................................................................ 12 Research Initiatives Documenting Environmental Changes in Arctic Regions ........................ 13

Arctic Systems Science Grids ............................................................................................... 13 International Tundra Experiment .......................................................................................... 13

Research in the Barrow Region ............................................................................................. 14 The Importance of Vegetation................................................................................................... 15

Individual Species ................................................................................................................. 15 Functional Groups ................................................................................................................. 16

Cover and Height ................................................................................................................... 16 Diversity ................................................................................................................................ 17

Documenting Vegetation Changes in Arctic Regions............................................................... 17 Climate Warming .................................................................................................................. 17

Multiple Community Types .................................................................................................. 18 Influence of Abiotic Factors on Vegetation .............................................................................. 19 Rationale for Study and Research Questions ............................................................................ 19

Methods......................................................................................................................................... 21 Study Area ................................................................................................................................. 21

Data Collection .......................................................................................................................... 21 Data Analyses ............................................................................................................................ 22

Site ......................................................................................................................................... 22

Grid ........................................................................................................................................ 23

Statistical Tests Performed ........................................................................................................ 24 Results ........................................................................................................................................... 28

Site ............................................................................................................................................. 28

Abiotic Correlations with Year and Correlations between Abiotic Variables ...................... 28 Vegetation Change over Time ............................................................................................... 28

Relation between Vegetation Change and Abiotic Factors ................................................... 29 Grid............................................................................................................................................ 30

Comparison between Community Types .............................................................................. 30 Discussion ..................................................................................................................................... 50 Conclusion .................................................................................................................................... 55 References ..................................................................................................................................... 58

7

List of Tables

Table 1: Correlations between mean summer air temperature (°C), total summer precipitation

(cm), the sum of thawing degree days (TDD), mean summer thaw depth (cm), mean summer soil

temperature (°C) and mean summer soil moisture (VWC %) during years 2010, 2012, 2013 and

2014 at the site. Bold values represent significant associations and rs values represent correlation

coefficients from Spearman Rank correlations. ............................................................................ 31

Table 2: Change in cover and diversity metrics over time at the site. A species within a

functional group was included if the species was found on at least 5 plots. Due to difficulty

identifying plants in the field, bryophytes and lichens were grouped by narrow growth form

rather than by species and pleurocarpous mosses included leafy liverworts. Values for change

over time represent the mean and standard error of the mean for each year. Bold p values

represent significant changes from analyses which included 1-way repeated measures ANOVAs

or Friedman tests and rs represents correlation coefficients from Spearman Rank correlations.

The sample size was 30 plots for each year. ................................................................................. 32

Table 3: Change in cover and height metrics over time at the site. Note that this analysis is

different from Table 2 because only plots where the plants were encountered are included

therefore the sample size may be less than 30. Due to difficulty identifying plants in the field,

bryophytes were grouped by narrow growth form rather than by species and pleurocarpous

mosses included leafy liverworts. Height was determined using the maximum height per plot.

Values for change over time represent the mean and standard error of the mean for each year.

Bold p values represent significant changes from analyses which included 1-way repeated

measures ANOVAs and rs values represent correlation coefficients from Spearman Rank

correlations. ................................................................................................................................... 33

Table 4: Correlations between cover or diversity metrics and mean summer air temperature (°C),

total summer precipitation (cm), the sum of thawing degree days (TDD), mean summer thaw

depth (cm), mean summer soil temperature (°C) and mean summer soil moisture (VWC %)

during years 2010, 2012, 2013 and 2014 at the site. Bold values represent significant associations

and rs values represent correlation coefficients from Spearman Rank correlations. The sample

size was 30 plots for each year. .................................................................................................... 34

Table 5: Correlations between cover or height metrics and mean summer air temperature (°C),



during years 2010, 2012, 2013 and 2014 at the site. Due to difficulty identifying plants in the

field, bryophytes were grouped by narrow growth form and pleurocarpous mosses included leafy

liverworts. Height was determined using the maximum height per plot. Bold values represent

significant associations and rs values represent correlation coefficients from Spearman Rank

correlations. ................................................................................................................................... 35

8

Table 6: Change in the cover of functional groups within four generalized plant communities

(dry, dry-moist, moist and wet) between 2010 and 2013 within the Arctic Systems Science grid.

The grid was comprised of 98 vegetation plots which both included and extended beyond the 30

vegetation plots at the site which were used for the prior analyses. Cover changes were analyzed

over time (Y), across community types (C) and the interaction between them (I) using a 2-way

ANOVA. Cover values represent the mean and standard error of the mean for each year. The

sample size was 24 for dry plots, 26 for dry-moist plots, 21 for moist plots and 27 for wet plots.

....................................................................................................................................................... 36

Table 7: Change in the cover of the most abundant taxa within four generalized plant

communities (dry, dry-moist, moist and wet) between 2010 and 2013 within the Arctic Systems

Science grid. The grid was comprised of 98 vegetation plots which both included and extended

beyond the 30 vegetation plots at the site which were used for the prior analyses. Due to

difficulty identifying plants in the field, bryophytes were grouped by narrow growth form and

pleurocarpous mosses included leafy liverworts. Cover changes were analyzed over time (Y),

across community types (C) and the interaction between them (I) using a 2-way ANOVA. Cover

values represent the mean and standard error of the mean for each year. .................................... 37

9

List of Figures

Figure 1: Maps of the study location (inset) and study vegetation plots near Barrow, Alaska.

Imagery was from Tweedie & Gaylord 2003, Manley et al. 2005 and the National Atlas of the

United States (http://nationalmap.gov). ........................................................................................ 25

Figure 2: Map of the vegetation plots at the site (symbols within the rectangle) and the Arctic

Systems Science grid which both included and extended beyond the site. Symbols represent the

four generalized community types represented by each plot while letters (B-K) and numbers (02-

11) identify the location of each plot within the grid. Generalized vegetation community types

were obtained from a land cover classification map in Tweedie et al. 2005, unpublished work

while imagery was from Tweedie & Gaylord 2003...................................................................... 26

Figure 3: Nonmetric multidimensional scaling (NMDS) of the four generalized vegetation

communities across the Arctic Systems Science grid in a) 2010 and b) 2013. The community

types were significantly different from each other in both years based on analysis of similarities

(ANOSIM). The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for moist plots and

27 for wet plots each year. ............................................................................................................ 27

Figure 4: Correlations between year and a) mean summer air temperature, b) total summer

precipitation, c) the sum of thawing degree days (TDD), d) mean summer thaw depth, e) mean

summer soil temperature and f) summer mean soil moisture at the site. Rs values represent

correlation coefficients from Spearman Rank correlations. Note the change in scale between

abiotic variables. ........................................................................................................................... 38

Figure 5: Mean cover of a) bryophytes, b) deciduous shrubs, c) forbs, d) graminoids, e) lichens,

f) litter and g) standing dead over time at the site. Different letters indicate statistical significance

between individual years. Statistical analyses were completed using 1-way repeated measures

ANOVAs or Friedman tests. † = log transformed;

‡ = square root transformed; * = non

parametric test. Error bars show standard error of the mean. The sample size was 30 plots for

each year. Note the change in scale between functional groups. .................................................. 39

Figure 6: Significant correlation between year and the cover of standing dead at the site. Rs value

represents correlation coefficient from a Spearman Rank correlation. The sample size was 30

plots for each year. ........................................................................................................................ 40

10

Figure 7: Significant correlation between the cover of forbs and mean summer soil temperature

during years 2010, 2012, 2013 and 2014 at the site. Rs value represents correlation coefficient

from a Spearman Rank correlation. The sample size was 30 plots for each year. ........................ 41

Figure 8: Significant correlations between the cover of graminoids and a) mean summer air

temperature, b) total summer precipitation, c) the sum of thawing degree days (TDD), d) mean

summer thaw depth and e) mean summer soil temperature during years 2010, 2012, 2013 and

2014 at the site. Rs values represent correlation coefficients from Spearman Rank correlations.

The sample size was 30 plots for each year. ................................................................................. 42 Figure 9: Significant correlations between the cover of litter and a) mean summer air

temperature, b) the sum of thawing degree days (TDD), c) mean summer soil temperature and d)

mean summer soil moisture during years 2010, 2012, 2013 and 2014 at the site. Rs values

represent correlation coefficients from Spearman Rank correlations. The sample size was 30

plots for each year. ........................................................................................................................ 43

Figure 10: Significant correlations between the cover of standing dead and a) mean summer air


summer thaw depth, e) mean summer soil temperature and f) mean summer soil moisture during

years 2010, 2012, 2013 and 2014 at the site. Rs values represent correlation coefficients from

Spearman Rank correlations. The sample size was 30 plots for each year ................................... 44

Figure 11: Significant correlations between cover of the abundant taxa Carex stans and a) mean

summer air temperature, b) total summer precipitation, c) the sum of thawing degree days

(TDD), d) mean summer thaw depth and e) mean summer soil temperature during years 2010,

2012, 2013 and 2014 at the site. Rs values represent correlation coefficients from Spearman Rank

correlations. The sample size was 22 plots for each year. ............................................................ 45

Figure 12: Significant correlations between cover of the abundant taxa Dupontia fisheri and a)

mean summer air temperature, b) the sum of thawing degree days (TDD) and c) mean summer

soil temperature during years 2010, 2012, 2013 and 2014 at the site. Rs values represent

correlation coefficients from Spearman Rank correlations. The sample size was 17 plots for each

year. ............................................................................................................................................... 46

Figure 13: Significant correlation between height of graminoids and mean summer soil moisture

during years 2010, 2012, 2013 and 2014 at the site. Height was determined using the maximum

height per plot. Rs value represents correlation coefficient from a Spearman Rank correlation.

The sample size was 30 plots for each year. ................................................................................. 47

11


f) litter and g) standing dead within four generalized plant communities (dry, dry-moist, moist

and wet) between 2010 and 2013 within the Arctic Systems Science grid. The grid was

comprised of 98 vegetation plots which both included and extended beyond the 30 vegetation

plots at the site which were used for the prior analyses. Cover changes were analyzed using a 2-

way ANOVA. ‡ = square root transformed. Lack of comparisons indicate functional group was

found in <5 plots and was subsequently excluded from analysis. Error bars show standard error

of the mean. The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for moist plots and

27 for wet plots. Note change in scale between the functional groups. ........................................ 48

Figure 15: Mean cover of a) acrocarpous mosses, b) pleurocarpous mosses, c) Carex stans, d)

Dupontia fisheri and e) Eriophorum russeolum within four generalized plant communities (dry,

dry-moist, moist and wet) between 2010 and 2013 within the Arctic Systems Science grid. The

grid was comprised of 98 vegetation plots which both included and extended beyond the 30


using a 2-way ANOVA. ‡ = square root transformed;

† = log transformed. Error bars show

standard error of the mean. The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for

moist plots and 27 for wet plots. Note change in scale between the abundant taxa. 49

12

Introduction

Numerous global environmental changes are occurring and one especially important

environmental change is climate warming. Arctic regions have been identified as one of the

regions most susceptible to climate warming. As such, several research initiatives and projects

have been implemented to document long-term changes in arctic regions, especially near

Barrow, Alaska. Below is a brief discussion of the topics most closely related to this project.

This thesis was completed as a subcomponent of a larger multi-university project aimed at

documenting and understanding the vegetation changes and ecosystem consequences occurring

in Northern Alaska. This project utilized an Arctic Systems Science Grid and was funded as part

of the International Tundra Experiment and the Arctic Observing Network.

Global Environmental Changes

A host of environmental changes are occurring across the globe mainly as a result of

anthropogenic activities. Nitrogen and phosphorus accumulation rates have increased

substantially over time in terrestrial and aquatic ecosystems (Bennett et al. 2001; Galloway et al.

2004). Another observed change is the establishment of numerous exotic species in new regions

through accidental or intentional relocation (Mills et al. 1993; Mack et al. 2000). Furthermore,

the frequency and intensity of fire regimes is shifting mainly as a result of fire suppression or

increased use of fires to alter the community composition of ecosystems (Veblen et al. 2000;

Marlon et al. 2008). Likewise, numerous landscapes have become fragmented over time through

activities such as deforestation which are known to alter the distributions of plant and animal

species (Skole & Tucker 1993; Fahrig 2003). In addition, anthropogenic sources have

substantially increased carbon dioxide and other greenhouse gas concentrations in the

atmosphere well above preindustrial levels; those environmental changes are especially

13

important in arctic regions which have been identified as particularly vulnerable to climate

warming (IPCC 2013). Climate warming is expected to be an especially strong driver of

environmental change in arctic regions due to the prevalence of cold-adapted species, short

growing seasons and soil nitrogen limitations (Callaghan et al. 2005).

Research Initiatives Documenting Environmental Changes in Arctic Regions

Arctic Systems Science Grids

The Arctic Systems Science (ARCSS) Program was established as a funding initiative by

the National Science Foundation (NSF) in the early 1990s. The goals of the program were to

investigate how physical, chemical, biological and sociocultural processes are influenced by

environmental changes in arctic regions and to identify ways to respond to such changes. One

component of the program was to establish spatial grids, which typically span 1 ha to 1-km2,

allowing for numerous research projects to conduct landscape-level research related to the

program goals. Currently this funding initiative supports interdisciplinary research at numerous

sites in arctic regions (https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=13426).

International Tundra Experiment

The International Tundra Experiment (ITEX) was established in 1990 to investigate the

response of vegetation and ecosystems to environmental change. The project mainly looked at

plant responses to environmental change at the species-level and across moisture gradient

extremes. Manipulated warming experiments occurred at each ITEX site to document the

impacts of climate warming on plants. The most common warming manipulation was through

the use of open-top chambers to passively warm the air 1-3°C consistent with future climate

warming predictions (Henry & Molau 1997). Protocols for various measurements were

https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=13426

14

developed to ensure consistent sampling at each site (Molau & Mølgaard 1996). Since all

research sites within the network contained a warming experiment and data were collected using

standardized protocols, syntheses that documented environmental change could be conducted

across space and time with the option of including warming as another variable (Elmendorf et al.

2012a). Since its inception, the ITEX network includes numerous research sites in both arctic

and alpine regions across more than 11 countries, and all arctic nations are currently represented

(http://ibis.geog.ubc.ca/itex/about.php).

Arctic Observing Network

The Arctic Observing Network (AON) began in 2006 as a funding initiative by the NSF

to encourage research proposals related to monitoring environmental change. The goals of AON

were threefold: to better understand and inform about current environmental changes already

occurring in the arctic, to predict future environmental changes and to develop ways to respond

to observed or predicted environmental changes. Aided by interest in the International Polar Year

(IPY), the NSF now funds 51 projects as part of AON including research into the atmosphere,

ocean and sea ice, hydrology and the cryosphere, terrestrial ecosystems and human dimensions.

ITEX projects in the United States and this project are currently funded through this initiative

(http://www.arcus.org/search-program/aon).

Research in the Barrow Region

A considerable amount of research initiatives and projects have been established near

Barrow, Alaska (a high arctic region) where this study took place. During the first IPY from

1882-1883, a weather station was built by explorers to monitor polar changes. The Naval Arctic

Research Laboratory was established in 1947 to provide infrastructure to encourage scientific

http://ibis.geog.ubc.ca/itex/about.php

http://www.arcus.org/search-program/aon

15

research in the region (Shelesnyak 1948). Research continued near Barrow and was especially

prevalent during the involvement of the United States in the International Biological Program

from 1964-1974 (USNC 1974). In 1973, the National Oceanic and Atmospheric Administration

began the Barrow, Alaska Observatory monitoring weather patterns and processes as well as

various aspects of the carbon cycle (http://www.esrl.noaa.gov/gmd/obop/brw/). In 1992, the

Ukpeaġvik Iñupiat Corporation set aside some of their privately owned land to form the Barrow

Environmental Observatory which was designated for research. This was rezoned as the

Scientific Research District in 2003 by the North Slope Borough. Each year, Barrow hosts nearly

40 different research projects and has received national and international recognition for its

cooperation with and fostering of scientific research (http://www.eu-interact.org/field-

sites/alaska-2/barrow/).

The Importance of Vegetation

Individual Species

Documenting vegetation changes is critical as plants are important species in biological

systems. They are primary producers exerting bottom up controls on successive trophic levels.

As such, plant community composition especially impacts herbivore forage quantity and quality

(Augustine & McNaughton 1998; Joly et al. 2009) as well as other plant-animal interactions

(Tylianakis et al. 2008). Plants also help determine habitat suitability for animals ultimately

influencing animal community composition (Orians & Wittenberger 1991; Rettie & Messier

2000). Plant species have also been shown to differ in resource allocation, impact nutrient cycles

and alter soil organic matter accumulation (Vinton & Burke 1995). Therefore, shifts in

vegetation species composition could have significant ecosystem consequences.

http://www.esrl.noaa.gov/gmd/obop/brw/

http://www.eu-interact.org/field-sites/alaska-2/barrow/

http://www.eu-interact.org/field-sites/alaska-2/barrow/

16

Functional Groups

Plant functional groups are used in ecological studies to serve as a reasonably accurate

representation of the traits and characteristics of individual species to describe functional

community composition. While some studies note that plant functional groups are not always

accurate representations of individual species (Chapin & Shaver 1985; Hollister et al. 2015),

benefits of using these groupings includes saving time when sampling vegetation and mapping

large-scale vegetation patterns, as well as allowing for useful coarse-grained modeling of

vegetation composition or plant traits (Lavorel et al. 2007; Ustin & Gamon 2010). Plant

functional groups are also commonly used in climate warming studies to predict future

consequences of environmental change, community structure and ecosystem function (Lavorel &

Garnier 2002; Walker et al. 2006; Dorrepaal 2007). Thus, the use of plant functional types as a

representation of several individual species has numerous benefits.

Cover and Height

Plant cover and height are important metrics for several reasons. Both impact competitive

interactions between species. For example, shading from taller or larger plants especially impacts

growth forms such as mosses and lichens due to their short stature (Cornelissen et al. 2001; Zona

et al. 2011). Cover and height also alter albedo by reducing the extent of snow cover, shifting the

timing of snow melt and scattering rather than reflecting short-wave radiation which has

implications for modifying the transfer of energy through ecosystems and enhancing climate

warming (Betts 2000; Loranty et al. 2011). Vegetation structure is important for the habitat

selection of a variety of animals such as birds and small mammals which rely on the cover and

height of vegetation for nesting and protection from predators (Cody 1981; Batzli & Pitelka

17

1983). Thus, vegetation cover and height are important metrics to consider when documenting

vegetation changes.

Diversity

Vegetation diversity is a valuable component of biological systems. It has been shown to

influence ecosystem function and the provision of ecosystem services such as food and timber

supply, erosion control, invasion resistance and pathogen regulation (Quijas et al. 2010). In

addition, plant diversity is linked to the productivity of ecosystems as the number of species

present at a site is related to biomass production (Cadotte et al. 2008; Marquard et al. 2009).

Likewise, plant diversity is associated with the stability of ecosystems as diverse systems can

continue to remain productive even when a few plant species are lost (Hector et al. 2010).

However, beyond a certain threshold, accumulated species losses lead to increased rates of

change within ecosystems (Cardinale et al. 2011; Reich et al. 2012). Furthermore, a loss of

vegetation diversity can strongly impact other trophic levels through bottom up controls

(Scherber et al. 2010). Finally, diversity metrics can help predict future plant and animal

assemblages which is imperative for ecosystem monitoring and management (Kessler et al.

2009; Cingolani et al. 2010). Therefore, monitoring vegetation diversity is critical.

Documenting Vegetation Changes in Arctic Regions

Climate Warming

Documenting vegetation changes in arctic regions is imperative. Northern regions have

been identified as the most vulnerable to increased climate warming (IPCC 2013) and vegetation

changes due to climate warming (either observational or experimental) have been well

documented. Studies have shown that arctic plants typically respond to warming with initial

18

responses of increased growth and reproduction, but this effect seems to lessen over time (Arft et

al. 1999; Kremers et al. 2015). Warming induced changes, such as alteration of the timing of

snow melt and the lengthening of the growing season are impacting the phenological activity of

plants (Walther 2010; Oberbauer et al. 2013). Vegetation community composition changes,

especially increased abundance and growth of shrubs have been linked with changes in albedo

which could serve as a positive feedback to climate warming (Chapin et al. 2005; Sturm et al.

2005). In addition, vegetation changes will likely impact the carbon storage of plants and the

release of carbon stored in soils due to increased microbial activity ultimately altering feedbacks

to climate change (Melillo et al. 2002; Davidson & Janssens 2006) and may turn tundra regions

into a carbon source instead of a carbon sink (Oechel et al. 1993).

Multiple Community Types

How vegetation is changing within multiple community types across the Barrow

landscape remains poorly understood. Most recent vegetation change studies already conducted

were investigated within one or two community types, typically dry heath tundra, wet meadow

tundra or both (as part of ITEX climate warming experiments). The study designs utilized

moisture gradient extremes to contrast changes between drier and wetter community types (e.g.

Hollister et al. 2005a; Hollister et al. 2005b). However, it is well known that more than just two

community types are prevalent across the landscape (Villarreal et al. 2012) and those

communities function differently (Lara et al. 2012). Thus, changes within the two commonly

used vegetation communities may not be representative of the landscape and vegetation changes

may be different when multiple communities are considered. Therefore, more research studies

are needed to better understand how vegetation is changing across the landscape within multiple

vegetation community types.

19

Influence of Abiotic Factors on Vegetation

Multiple abiotic factors can also influence vegetation community composition and the

diversity of ecosystems. Soil temperature is correlated with plant phenology (Oberbauer et al.

1998), net primary productivity (Natali et al. 2012) and nutrient availability (Melillo et al. 2002),

and may help direct community composition (Brooker & van der Wal 2003). Likewise,

precipitation and soil moisture is associated with plant growth (Phoenix et al. 2001), species

composition and species interactions (Gornall et al. 2007) as well as net primary productivity and

diversity (Weltzin et al. 2003; Douma et al. 2007). Together, both soil temperature and soil

moisture are important factors controlling litter decomposition rates which will impact processes

such as soil carbon storage and release (Hobbie 1996; Shaver et al. 2006). In addition, air

temperature is linked with changes in the phenology (Oberbauer et al. 2013), growth (Hollister &

Flaherty 2010) reproductive success (Myers-Smith et al. 2011) and distributions of plants

(Parmesan & Yohe 2003). Likewise, thaw depth influences vegetation growth rates and

community composition (Anisimov & Reneva 2006; Schuur et al. 2007). Therefore, identifying

how various abiotic factors influence vegetation community composition is critical.

Rationale for Study and Research Questions

Continued research in the arctic is imperative as the region is particularly susceptible to

climate warming due to factors such as the prevalence of cold-adapted species, the short growing

season and soil nutrient limitations. Identifying vegetation changes in arctic regions warrants

further investigation at the plant species level, as plant species exert bottom-up controls on

successive trophic levels, determine habitat suitability for animals and influence soil processes;

as well as at the functional group level, as utilizing functional groups saves time when sampling

vegetation, simplifies vegetation modeling and mapping and has predictive power regarding

20

future environmental, community structure and ecosystem function changes. Specifically,

changes in the cover and height of vegetation should be documented as both metrics influence

competitive interactions between species, alter albedo and provide nesting habitat and protection

from predators for various animals. In addition, research into vegetation diversity is critical as it

has been linked to the productivity, stability and function of ecosystems and vegetation diversity

controls the diversity of successive trophic levels. Likewise, investigating the relationships

between vegetation metrics and abiotic variables is warranted to explain why changes in

vegetation metrics are occurring. Finally, there is a continued need to identify landscape level

vegetation changes within multiple vegetation community types, especially in the Barrow region.

This study addresses those critical research needs by documenting vegetation changes near

Barrow, Alaska. Specifically, the following research questions were addressed:

1) How has the vegetation metrics of cover, height and diversity changed over time?

2) What abiotic factors help explain the observed changes?

3) Are observed changes over time consistent across community types?

21

Methods

Study Area

Permanent sampling locations were established by other researchers in the early 1990s at

approximately 100 points forming an Arctic Systems Science spatial grid (Brown et al. 2000). In

2010, 98 vegetation plots were implemented near those points and were utilized for this study

(Figure 1). The 1-m2 vegetation plots were spaced 100 m apart and spanned approximately 1

km2. No treatment was assigned and the plots were distributed across multiple vegetation

community types. The mean July temperature at Barrow was ~4oC (Brown et al. 1980). Plots

were usually snow free in early to mid-June and maximum thaw depth was typically between 20

and 80cm. Topography varied across the landscape and included a drained thaw lake, a well-

drained ridge, various ponds and polygonized tundra. Dominant vegetation varied across the grid

but generally includes Carex stans, Dupontia fisheri, Eriophorum triste, Eriophorum russeolum

and Poa arctica.

Data Collection

Vegetation cover, height and diversity metrics were estimated using a point frame

method (standardized in Walker 1996). The frame was 75-cm2 and utilized 100 string

intersections spaced 7.5 cm apart. The frame was leveled and aligned with permanent markers

which were placed at the four corners and at three points in each plot to allow for reasonably

consistent re-sampling between years. A graduated ruler was used to measure the height of each

encounter relative to the ground measurement at each point. All plants were identified to species

and the live or dead status was recorded. In 2010 and 2013, 98 vegetation plots were sampled; in

2012 and 2014, only 30 of these plots were sampled. Sampling was done near peak season in

July and August within seven calendar days of previous years to minimize the difference in

22

development between samplings. For all analyses, ‘site’ refers to the 30 vegetation plots sampled

in 2010, 2012, 2013 and 2014 while ‘grid’ refers to all the 98 vegetation plots which were

sampled in 2010 and 2013. Therefore, the grid includes the 30 site plots and includes the 68 plots

which extend beyond the site (n=98).

Air temperature (°C) was collected from a Model 107 Temperature Probe and

precipitation (cm) from a TE525 tipping bucket rain gage located within the Arctic Systems

Science Grid. Readings were taken every 15 minutes and averaged hourly (temperature) or

summed (precipitation) using a CR10X Datalogger (Campbell Scientific, Logan, Utah USA).

Thaw depth (cm) was recorded for each plot near the end of the field seasons by inserting a

stainless steel graduated rod into the ground until the frozen surface was reached. Soil moisture

and soil temperature were measured repeatedly during the summer using a FieldScout TDR 300

soil moisture meter and a standard household-grade thermometer, respectively.

Data Analyses

Site

Cover was calculated as the sum of the plant encounters in each plot. Prior to analyses,

plant encounters were grouped as follows: functional groups of bryophytes, deciduous shrubs,

forbs, graminoids, lichens, litter or standing dead; species within functional groups; and the most

abundant taxa. All encounters were included in the analysis; however, information on a species is

only presented if it was found on more than five plots each year. Due to difficulty identifying

plants in the field, bryophytes and lichens were grouped by narrow functional groups and

pleurocarpous mosses included leafy liverworts. Taxa were considered abundant if they were

found on at least half of the plots each year. A cover value of 0 was assigned for analysis if a

functional group or species was not present in a particular plot. For the analyses involving

23

abundant taxa, if taxa were not found on a particular plot each year the plot was excluded from

analysis (a value of 0 was not assigned).

The height (cm) of vascular plants was calculated by subtracting the ground measurement

from the height of the vegetation encounter at each point. Only the maximum height per plot was

used for analysis.

Alpha diversity was calculated as the total number of vascular species present per plot.

Beta diversity was determined by dividing gamma diversity (the total number of vascular plant

species at the site each year) by alpha diversity for each plot. Equitability was determined by

calculating Shannon’s equitability (EH) for each plot.

Air temperature values were obtained by averaging daily summer air temperatures each

year. Precipitation was calculated as the sum of daily summer precipitation values. Thawing

degree days were calculated as the sum of the departures of daily temperatures above 0 degrees

Celsius. Thaw depth, soil temperature and soil moisture measurements from each plot collected

during the summers were averaged each year. The summer was defined as the period from when

the plots were snow free until August 15.

Grid

Plots were grouped into generalized vegetation communities based on a land cover

classification map developed from 2002 QuickBird satellite imagery (Tweedie et al. 2005,

unpublished work) which was modified based on field observations of each plot collected during

the 2010-2014 field seasons (Figure 2). Four vegetation communities were apparent: dry dwarf

shrub-graminoid tundra (dry, n=24), dry-moist dwarf shrub-graminoid tundra (dry-moist, n=26),

moist graminoid tundra (moist, n=21) and wet graminoid tundra (wet, n=27). Vegetation cover

was calculated with the same groups as described above, however, change in cover of species

24

within functional groups was not investigated. Analysis of similarities (ANOSIM) after

nonmetric multidimensional scaling (NMDS) showed that the vegetation communities were

significantly different from each other in years 2010 and 2013 (Figure 3).

Statistical Tests Performed

Data was checked for a normal distribution using the Shapiro-Wilk normality test and for

equal variance using Bartlett’s test before analyses were conducted. If necessary, data were

square root or log transformed to approximate normality. If normality could not be

approximated, non-parametric statistical tests were used. Post hoc analyses were completed

using paired t-tests or paired Mann-Whitney U tests. The Holm method was used to correct for

multiple comparisons. For all analyses, an alpha level of 0.05 was used to determine

significance. All statistical analyses were performed in R Software for Statistical Computing v.

3.1.1 (R Development Core Team 2014). The R package ‘vegan’ was used to calculate

Shannon’s equitability values (Oksanen et al. 2013).

The change in cover of functional groups, species within functional groups, abundant

taxa and the change in height of vascular functional groups as well as changes in alpha diversity,

beta diversity and equitability over time at the site were analyzed using 1-way repeated measures

ANOVAs or Friedman tests. Changes in the cover of functional groups and abundant taxa over

time, across community types and the interaction between year and community type across the

grid were analyzed using a 2 way ANOVA.

Spearman Rank correlations were used to investigate relationships between vegetation

metrics and abiotic variables, relationships between vegetation metrics and year, relationships

between all possible pairs of abiotic variables, as well as relationships between abiotic variables

and year.

25

Figure 1: Maps of the study location (inset) and study vegetation plots near Barrow, Alaska.

Imagery was from Tweedie & Gaylord 2003, Manley et al. 2005 and the National Atlas of the

United States (http://nationalmap.gov).

http://nationalmap.gov/

26

Figure 2: Map of the vegetation plots at the site (symbols within the rectangle) and the Arctic

Systems Science grid which both included and extended beyond the site. Symbols represent the

four generalized community types represented by each plot while letters (B-K) and numbers (02-

11) identify the location of each plot within the grid. Generalized vegetation community types

were obtained from a land cover classification map in Tweedie et al. 2005, unpublished work

while imagery was from Tweedie & Gaylord 2003.

27

Figure 3: Nonmetric multidimensional scaling (NMDS) of the four generalized vegetation

communities across the Arctic Systems Science grid in a) 2010 and b) 2013. The community

types were significantly different from each other in both years based on analysis of similarities

(ANOSIM). The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for moist plots and

27 for wet plots each year.

a)

b)

28

Results

Site

Abiotic Correlations with Year and Correlations between Abiotic Variables

No abiotic variable was correlated significantly with year (Figure 4). When correlations

between all possible pairs of abiotic variables were investigated, only mean summer air

temperature and the sum of thawing degree days were significantly correlated (Table 1).

Vegetation Change over Time

The cover of all functional groups and most species within functional groups changed

significantly over time. Most notably, the mean cover of graminoids more than doubled from

44.4% to 92.1% from years 2010 to 2013 followed by an approximate halving of cover to 50.1%

in the year 2014. In addition, the mean cover of standing dead more than doubled from 49.6% to

105.8% between years 2010 and 2012 but declined sharply to 29.5% in the year 2013. Likewise,

the cover of Carex stans more than doubled from 17.7% to 40.0% in years 2010 to 2013, but was

reduced to 17.2% in the year 2014. (Table 2, Figure 5). The cover of the abundant taxa of

pleurocarpous mosses and Carex stans changed significantly over time. The change in mean

cover of Carex stans was notable as it more than doubled from 24.1% to 54.0% between years

2010 and 2013. By the year 2014, the mean cover of Carex stans was reduced to 23.1% (Table

3). There was a significant correlation between cover and year for standing dead but no

significant correlations between cover and year for any of the other functional groups, all species

within functional groups or all abundant taxa (Table 2, Table 3, Figure 6).

The height of graminoids changed significantly while deciduous shrub and forb height

did not change significantly over time. Of particular interest was the mean height of graminoids

29

increased by 3.2 cm from the years 2010 to 2012. There were no significant correlations

between height and year for any of the three functional groups (Table 3).

Alpha diversity changed significantly but beta diversity and equitability did not change

significantly over time. The largest change in alpha diversity was a shift from an average of 6.7

to 8.1 vascular plant species per plot between years 2012 and 2013. None of the three diversity

metrics showed significant correlations with year (Table 2).

Relation between Vegetation Change and Abiotic Factors

There was a significant correlation between the cover of forbs and mean summer soil

temperature (Table 4, Figure 7). The cover of graminoids was correlated significantly with all

abiotic variables except mean summer soil moisture but was most strongly correlated with mean

summer soil temperature (Table 4, Figure 8). There were significant correlations between the

cover of litter and mean summer air temperature, the sum of thawing degree days, mean summer

soil temperature and mean summer soil moisture. The cover of litter was most strongly

correlated with mean summer soil temperature (Table 4, Figure 9). The cover of standing dead

was correlated significantly with all measured abiotic variables and most strongly correlated with

total summer precipitation (Table 4, Figure 10). The cover of bryophytes, deciduous shrubs and

lichens were not correlated significantly with any of the measured abiotic variables (Table 4).

There were significant correlations between the cover of the abundant taxa Carex stans and all

measured abiotic variables except mean summer soil moisture but was most strongly correlated

with mean summer soil temperature (Table 5, Figure 11). In addition, the cover of the abundant

taxa Dupontia fisheri was significantly correlated with mean summer air temperature, the sum of

thawing degree days, and mean summer soil temperature but the most strongly correlated with

mean summer air temperature and the sum of thawing degree days (Table 5, Figure 12). The

30

cover of all other abundant taxa was not correlated significantly with any of the measured abiotic

variables (Table 5).

The height of graminoids was correlated significantly with mean summer soil moisture.

The height of deciduous shrubs and forbs were not correlated significantly with any of the

measured abiotic variables (Table 5, Figure 13).

All three diversity metrics were not correlated significantly with any of the measured

abiotic variables (Table 4).

Grid

Comparison between Community Types

Although there were significant changes in cover over time and differences in cover

within community types for most of the functional groups and the abundant taxa, there was no

interaction between year and community type for any of the functional groups or the abundant

taxa (Table 6, Table 7, Figure 14, Figure 15).

31

Table 1: Correlations between mean summer air temperature (°C), total summer precipitation

(cm), the sum of thawing degree days (TDD), mean summer thaw depth (cm), mean summer soil

temperature (°C) and mean summer soil moisture (VWC %) during years 2010, 2012, 2013 and

2014 at the site. Bold values represent significant associations and rs values represent correlation

coefficients from Spearman Rank correlations.

Precipitation TDD (sum)

Thaw depth

Soil

temperature

Soil moisture

rs p rs p rs p rs p rs p

Air temperature -0.40 0.75 0.99 0.01 0.40 0.75 0.80 0.33 -0.45 0.75 Precipitation — — -0.40 0.75 0.40 0.75 0.20 0.92 0.40 0.75 TDD (sum) — — — — 0.40 0.75 0.80 0.33 -0.40 0.75 Thaw depth — — — — — — 0.80 0.33 0.60 0.42 Soil temperature — — — — — — — — <0.01 0.99

32

Table 2: Change in cover and diversity metrics over time at the site. A species within a

functional group was included if the species was found on at least 5 plots. Due to difficulty

identifying plants in the field, bryophytes and lichens were grouped by narrow growth form

rather than by species and pleurocarpous mosses included leafy liverworts. Values for change

over time represent the mean and standard error of the mean for each year. Bold p values

represent significant changes from analyses which included 1-way repeated measures ANOVAs

or Friedman tests and rs represents correlation coefficients from Spearman Rank correlations.

The sample size was 30 plots for each year.

Change Over Time ANOVA Correlation

Metric 2010 2012 2013 2014 p rs p

Vegetation cover Bryophytes 34.5 (3.2) 41.9 (4.8) 44.8 (4.1) 37.4 (3.3) 0.05 0.07 0.47 Acrocarpous mosses

‡ 17.6 (2.9) 14.1 (2.7) 19.1 (2.5) 13.6 (2.2) 0.88 -0.01 0.92

Pleurocarpous mosses‡ 16.4 (3.8) 27.4 (5.2) 24.8 (4.3) 23.5 (3.9) <0.01 0.16 0.08

Deciduous shrubs* 7.4 (2.4) 9.0 (3.1) 9.9 (3.2) 7.8 (2.6) <0.01 0.05 0.58 Salix rotundifolia* 5.8 (2.0) 6.4 (2.1) 7.9 (2.5) 6.6 (2.3) 0.01 0.03 0.74 Forbs* 6.7 (1.4) 11.0 (3.4) 14.0 (3.2) 6.7 (1.7) <0.01 <0.01 0.97 Petasites frigidus* 1.8 (0.7) 5.8 (3.1) 4.3 (2.1) 2.3 (1.2) 0.19 0.01 0.87 Saxifraga cernua* 1.4 (0.5) 1.0 (0.4) 2.2 (0.6) 0.8 (0.2) 0.01 0.01 0.89 Saxifraga foliolosa* 0.3 (0.2) 0.5 (0.3) 0.9 (0.3) 0.5 (0.3) 0.13 0.09 0.31 Stellaria spp.* 1.9 (0.6) 1.6 (0.6) 2.7 (1.0) 1.5 (0.5) 0.20 0.01 0.89 Graminoids

† 44.4 (4.0) 69.6 (7.6) 92.1 (7.5) 50.1 (4.6) <0.01 0.13 0.17

Arctagrostis latifolia* 2.0 (0.8) 2.6 (0.9) 3.2 (1.1) 1.8 (0.8) 0.01 <0.01 0.96 Carex stans* 17.7 (2.9) 27.6 (4.5) 40.0 (6.1) 17.2 (2.7) <0.01 0.04 0.68 Dupontia fisheri* 6.8 (1.7) 9.9 (2.5) 10.2 (2.8) 6.2 (1.7) <0.01 -0.04 0.69 Eriophorum triste* 2.7 (0.9) 9.0 (3.0) 8.1 (2.8) 6.7 (2.3) <0.01 0.12 0.18 Eriophorum russeolum* 5.2 (1.0) 8.0 (2.7) 11.0 (2.9) 7.9 (2.1) 0.02 0.05 0.62 Luzula arctica* 2.0 (0.9) 3.2 (1.2) 4.3 (1.9) 1.8 (1.0) <0.01 <0.01 0.97 Luzula confusa* 1.7 (0.5) 1.6 (0.5) 2.4 (0.8) 1.0 (0.4) 0.03 -0.12 0.21 Poa spp.* 4.8 (1.4) 6.4 (2.9) 11.2 (3.8) 6.1 (2.0) <0.01 0.07 0.45 Lichens* 12.8 (3.1) 15.2 (3.3) 19.6 (4.3) 15.3 (3.3) <0.01 0.03 0.73 Foliose* 3.3 (0.9) 5.2 (1.4) 6.6 (1.7) 4.6 (1.3) 0.01 0.04 0.65 Fruticose* 9.4 (2.5) 9.8 (2.5) 12.5 (3.1) 10.3 (2.4) <0.01 0.04 0.63 Litter

‡ 49.1 (3.9) 29.2 (3.9) 27.6 (3.7) 39.2 (3.2) <0.01 -0.14 0.12

Standing dead* 49.6 (4.1) 105.8 (7.7) 29.5 (3.6) 53.3 (4.3) <0.01 -0.19 0.03 Diversity Alpha 7.1 (0.4) 6.7 (0.4) 8.1 (0.5) 7.3 (0.5) <0.01 0.04 0.69 Beta 0.26 (0.01) 0.26 (0.01) 0.27 (0.01) 0.26 (0.01) 0.56 -0.01 0.93 Equitability

‡ 0.43 (0.02) 0.40 (0.02) 0.41 (0.02) 0.41 (0.02) 0.80 -0.08 0.36

† = log transformed ‡ = square root transformed

* = non parametric test

33

Table 3: Change in cover and height metrics over time at the site. Note that this analysis is

different from Table 2 because only plots where the plants were encountered are included

therefore the sample size may be less than 30. Due to difficulty identifying plants in the field,

bryophytes were grouped by narrow growth form rather than by species and pleurocarpous

mosses included leafy liverworts. Height was determined using the maximum height per plot.

Values for change over time represent the mean and standard error of the mean for each year.

Bold p values represent significant changes from analyses which included 1-way repeated

measures ANOVAs and rs values represent correlation coefficients from Spearman Rank

correlations.

Change Over Time ANOVA Correlation

Metric 2010 2012 2013 2014 p rs p n

Abundant taxa cover Acrocarpous mosses

‡ 21.9 (3.1) 16.1 (3.1) 21.4 (2.8) 16.1 (2.5) 0.13 -0.07 0.52 23

Pleurocarpous mosses‡ 22.3 (4.6) 35.2 (6.2) 32.2 (5.0) 30.4 (4.3) <0.01 0.16 0.14 22

Carex stans‡ 24.1 (3.0) 37.2 (4.7) 54.0 (6.0) 23.1 (2.7) <0.01 0.05 0.61 22

Dupontia fisheri† 11.4 (2.5) 16.9 (3.7) 17.4 (4.2) 10.7 (2.8) 0.98 -0.05 0.68 17

Eriophorum russeolum†

8.0 (1.3) 14.9 (4.5) 18.4 (4.4) 13.9 (3.3) 0.12 0.15 0.22 16 Height (cm) Deciduous shrubs 1.9 (0.4) 3.0 (0.6) 2.2 (0.4) 2.3 (0.4) 0.31 0.08 0.64 9 Forbs

‡ 5.1 (0.8) 6.2 (1.1) 4.7 (0.7) 3.7 (0.6) 0.14 -0.17 0.12 22

Graminoids‡ 11.1 (0.7) 14.3 (0.9) 13.0 (0.9) 13.2 (1.0) 0.01 0.12 0.20 30

† = log transformed ‡ = square root transformed n = the sample size for each year

34

Table 4: Correlations between cover or diversity metrics and mean summer air temperature (°C),



during years 2010, 2012, 2013 and 2014 at the site. Bold values represent significant associations

and rs values represent correlation coefficients from Spearman Rank correlations. The sample

size was 30 plots for each year.

Air temperature

Precipitation

TDD (sum)

Thaw depth

Soil temperature

Soil moisture

Metric rs p rs p rs p rs p rs p rs p

Vegetation cover Bryophytes 0.09 0.33 0.09 0.36 0.09 0.33 0.08 0.37 0.14 0.14 -0.04 0.63 Deciduous shrubs 0.02 0.82 0.04 0.65 0.02 0.82 0.01 0.89 0.04 0.66 -0.03 0.73 Forbs 0.12 0.20 0.09 0.34 0.12 0.20 0.15 0.09 0.18 0.04 0.01 0.88 Graminoids 0.27 <0.01 0.22 0.02 0.27 <0.01 0.27 <0.01 0.40 <0.01 -0.09 0.33 Lichens 0.03 0.74 0.03 0.73 0.03 0.74 0.02 0.80 0.05 0.61 -0.02 0.81 Litter -0.33 <0.01 -0.07 0.46 -0.33 <0.01 -0.12 0.21 -0.34 <0.01 0.26 <0.01 Standing dead 0.27 <0.01 -0.67 <0.01 0.27 <0.01 -0.45 <0.01 -0.18 0.05 -0.50 <0.01 Diversity Alpha -0.01 0.87 0.16 0.07 -0.01 0.87 0.14 0.11 0.10 0.28 0.11 0.23 Beta 0.04 0.69 0.03 0.78 0.04 0.69 0.05 0.56 0.06 0.53 0.01 0.88 Equitability -0.04 0.66 0.01 0.96 -0.04 0.66 0.06 0.52 -0.02 0.86 0.12 0.19

35

Table 5: Correlations between cover or height metrics and mean summer air temperature (°C), total summer precipitation (cm), the

sum of thawing degree days (TDD), mean summer thaw depth (cm), mean summer soil temperature (°C) and mean summer soil

moisture (VWC %) during years 2010, 2012, 2013 and 2014 at the site. Due to difficulty identifying plants in the field, bryophytes

were grouped by narrow growth form and pleurocarpous mosses included leafy liverworts. Height was determined using the

maximum height per plot. Bold values represent significant associations and rs values represent correlation coefficients from

Spearman Rank correlations.

Air

temperature

Precipitation TDD

(sum) Thaw

Depth Soil

temperature Soil

moisture

Metric rs p rs p rs p rs p rs p rs p n

Abundant taxa cover Acrocarpous mosses -0.03 0.80 0.11 0.29 -0.03 0.80 0.17 0.10 0.07 0.48 0.19 0.07 23 Pleurocarpous mosses 0.05 0.61 0.06 0.59 0.05 0.61 -0.05 0.66 0.06 0.59 -0.16 0.15 22 Carex stans 0.42 <0.01 0.22 0.04 0.42 <0.01 0.41 <0.01 0.57 <0.01 -0.08 0.47 22 Dupontia fisheri

0.25 0.05 -0.01 0.92 0.25 0.05 0.15 0.21 0.24 0.05 -0.07 0.59 17

Eriophorum russeolum 0.04 0.75 0.12 0.32 0.04 0.75 0.03 0.82 0.10 0.45 -0.08 0.51 16 Height (cm) Deciduous shrubs 0.09 0.62 -0.08 0.66 0.09 0.62 -0.11 0.51 <0.01 0.96 -0.20 0.24 9 Forbs 0.17 0.11 -0.11 0.30 0.17 0.11 0.11 0.32 0.13 0.23 <0.01 0.95 22 Graminoids 0.15 0.11 -0.03 0.74 0.15 0.11 -0.06 0.49 0.09 0.30 -0.23 0.01 30

n = sample size each year

36

Table 6: Change in the cover of functional groups within four generalized plant communities (dry, dry-moist, moist and wet) between

2010 and 2013 within the Arctic Systems Science grid. The grid was comprised of 98 vegetation plots which both included and

extended beyond the 30 vegetation plots at the site which were used for the prior analyses. Cover changes were analyzed over time

(Y), across community types (C) and the interaction between them (I) using a 2-way ANOVA. Cover values represent the mean and

standard error of the mean for each year. The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for moist plots and 27 for

wet plots.

Dry Dry-Moist Moist Wet ANOVA (p Value)

Metric 2010 2013 2010 2013 2010 2013 2010 2013 Y C I

Vegetation cover Bryophytes

‡ 28.0 (3.0) 42.4 (4.4) 34.2 (3.2) 44.1 (4.0) 38.1 (3.7) 60.6 (5.3) 40.9 (4.0) 67.2 (4.9) <0.01 <0.01 0.37

Deciduous shrubs‡ 6.7 (2.9) 7.3 (3.0) 6.5 (2.5) 10.3 (4.0) — — — — 0.54 <0.01 0.94

Forbs‡ 12.2 (2.5) 20.2 (4.9) 8.1 (1.9) 14.7 (3.3) 10.3 (3.2) 20.3 (3.8) 5.2 (1.6) 10.2 (2.3) <0.01 0.01 0.85

Graminoids‡ 41.0 (4.8) 81.4 (6.6) 40.0 (4.5) 80.0 (7.1) 54.3 (6.4) 104.8 (9.3) 52.4 (5.3) 101.8 (8.1) <0.01 <0.01 0.98

Lichens‡ 19.9 (3.2) 29.0 (4.6) 16.7 (3.3) 28.4 (5.0) — — — — 0.11 <0.01 0.40

Litter‡ 45.0 (3.1) 19.8 (1.8) 42.9 (3.2) 21.9 (2.3) 55.2 (3.9) 29.4 (3.1) 52.0 (4.1) 30.3 (4.2) <0.01 <0.01 0.87

Standing dead‡ 50.0 (2.5) 30.9 (3.3) 55.3 (3.6) 24.8 (2.1) 48.5 (6.0) 27.3 (2.4) 41.5 (4.8) 26.0 (4.0) <0.01 0.08 0.21

‡ = square root transformed

— = functional group was found in <5 plots and was subsequently excluded from analysis

37

Table 7: Change in the cover of the most abundant taxa within four generalized plant communities (dry, dry-moist, moist and wet)

between 2010 and 2013 within the Arctic Systems Science grid. The grid was comprised of 98 vegetation plots which both included

and extended beyond the 30 vegetation plots at the site which were used for the prior analyses. Due to difficulty identifying plants in

the field, bryophytes were grouped by narrow growth form and pleurocarpous mosses included leafy liverworts. Cover changes were

analyzed over time (Y), across community types (C) and the interaction between them (I) using a 2-way ANOVA. Cover values

represent the mean and standard error of the mean for each year.

Dry Dry-Moist Moist Wet ANOVA (p Value)

Metric 2010 2013 n 2010 2013 n 2010 2013 n 2010 2013 n Y C I

Abundant taxa cover Acrocarpous mosses

‡ 23.3 (3.0) 29.2 (3.8) 24 24.0 (2.8) 26.4 (3.2) 25 17.8 (3.8) 24.2 (4.0) 17 14.6 (3.0) 24.1 (3.0) 19 0.01 0.09 0.51

Pleurocarpous mosses‡ 5.7 (1.1) 16.5 (3.1) 18 16.1 (3.9) 24.3 (5.3) 16 24.6 (4.0) 37.1 (4.4) 20 29.4 (3.7) 41.1 (4.1) 25 <0.01 <0.01 0.91

Carex stans‡ 9.7 (2.2) 22.9 (5.6) 17 14.3 (2.9) 32.6 (6.5) 19 14.3 (3.4) 35.4 (6.5) 15 24.5 (3.8) 56.4 (7.6) 20 <0.01 <0.01 0.84

Dupontia fisheri‡ 10.5 (2.1) 10.6 (2.7) 12 16.1 (3.8) 21.3 (5.2) 15 18.6 (3.1) 25.4 (3.5) 21 18.3 (3.9) 25.0 (4.8) 24 0.06 0.03 0.84

Eriophorum russeolum†

6.4 (1.2) 12.6 (3.0) 17 9.9 (1.7) 18.9 (4.9) 17 9.0 (2.7) 20.5 (4.3) 19 8.1 (1.4) 19.5 (3.5) 23 <0.01 <0.01 0.91 † = log transformed ‡ = square root transformed n = the sample size for each year

38

Figure 4: Correlations between year and a) mean summer air temperature, b) total summer

precipitation, c) the sum of thawing degree days (TDD), d) mean summer thaw depth, e) mean

summer soil temperature and f) summer mean soil moisture at the site. Rs values represent

correlation coefficients from Spearman Rank correlations. Note the change in scale between

abiotic variables.

0

2

4

6

Air T

em

pe

ratu

re (°C

) rs=-0.40, p=0.75 a)

0

8

16

24

Pre

cip

ita

tio

n (

cm

)

b) rs=0.60, p=0.42

0

140

280

420

TD

D (

su

m)

rs=-0.40, p=0.75 c)

0

15

30

45

Th

aw

De

pth

(cm

)

rs=-0.40, p=0.75 d)

0

2

4

6

2009201020112012201320142015

So

il T

em

pe

ratu

re (°C

)

Year

rs=-0.20, p=0.92 e)

0

25

50

75

2009 2010 2011 2012 2013 2014 2015So

il M

ois

ture

(V

WC

%)

Year

rs=-0.40, p=0.75 f)

39


f) litter and g) standing dead over time at the site. Different letters indicate statistical significance

between individual years. Statistical analyses were completed using 1-way repeated measures

ANOVAs or Friedman tests. † = log transformed; ‡ = square root transformed; * = non parametric

test. Error bars show standard error of the mean. The sample size was 30 plots for each year.

Note the change in scale between functional groups.

0

20

40

60

Co

ve

r (%

) a) bryophytes

a a,b

b,c a,b

0

5

10

15

b) deciduous shrubs*

a a,b b,c

a,b,c

0

10

20

30

Co

ve

r (%

)

c) forbs*

a a,b

c a

0

35

70

105

d) graminoids†

a

b

c

a

0

10

20

30

Co

ve

r (%

)

e) lichens*

a a,b

c b

0

40

80

120

2010 2012 2013 2014

Co

ve

r (%

)

Year

g) standing dead*

a

b

c

a

0

20

40

60

2010 2012 2013 2014

Year

f) litter‡

a

b b,c d

40

Figure 6: Significant correlation between year and the cover of standing dead at the site. Rs value

represents correlation coefficient from a Spearman Rank correlation. The sample size was 30

plots for each year.

0

80

160

240

2009 2010 2011 2012 2013 2014 2015

Co

ve

r (%

)

Year

rs=-0.19, p=0.03

41

Figure 7: Significant correlation between the cover of forbs and mean summer soil temperature

during years 2010, 2012, 2013 and 2014 at the site. Rs value represents correlation coefficient

from a Spearman Rank correlation. The sample size was 30 plots for each year.

0

35

70

105

2.4 3.1 3.8 4.5

Co

ve

r (%

)

Soil Temperature (°C)

rs=0.18, p=0.04

42

Figure 8: Significant correlations between the cover of graminoids and a) mean summer air


summer thaw depth and e) mean summer soil temperature during years 2010, 2012, 2013 and

2014 at the site. Rs values represent correlation coefficients from Spearman Rank correlations.


0

70

140

210

2 3.5 5 6.5

Co

ve

r (%

)

Air Temperature (°C)

a) rs=0.27, p<0.01

0

70

140

210

6 12 18 24

Precipitation (cm)

rs=0.22, p=0.02 b)

0

70

140

210

180 260 340 420

Co

ve

r (%

)

TDD (sum)

c) rs=0.27, p<0.01

0

70

140

210

34 37 40 43

Thaw Depth (cm)

rs=0.27, p<0.01 d)

0

70

140

210

2.2 3 3.8 4.6

Co

ve

r (%

)


e) rs=0.40, p<0.01

43

Figure 9: Significant correlations between the cover of litter and a) mean summer air

temperature, b) the sum of thawing degree days (TDD), c) mean summer soil temperature and d)

mean summer soil moisture during years 2010, 2012, 2013 and 2014 at the site. Rs values

represent correlation coefficients from Spearman Rank correlations. The sample size was 30

plots for each year.

0

50

100

150

2 3.5 5 6.5

Co

ve

r (%

)


a) rs=-0.33, p<0.01

0

50

100

150

170 270 370 470

TDD (sum)

b) rs=-0.33, p<0.01

0

50

100

150

2 3 4 5

Co

ve

r (%

)


c) rs=-0.34, p<0.01

0

50

100

150

40 50 60 70

Soil Moisture (VWC %)

d) rs=0.26, p<0.01

44

Figure 10: Significant correlations between the cover of standing dead and a) mean summer air


summer thaw depth, e) mean summer soil temperature and f) mean summer soil moisture during

years 2010, 2012, 2013 and 2014 at the site. Rs values represent correlation coefficients from

Spearman Rank correlations. The sample size was 30 plots for each year.

0

70

140

210

2 3.5 5 6.5

Co

ve

r (%

)


a) rs=0.27, p<0.01

0

70

140

210

6 12 18 24

Precipitation (cm)

rs=-0.67, p<0.01 b)

0

70

140

210

170 270 370 470

Co

ve

r (%

)

TDD (sum)

c) rs=0.27, p<0.01

0

70

140

210

33 36 39 42

Thaw Depth (cm)

d) rs=-0.45, p<0.01

0

70

140

210

2 3 4 5

Co

ve

r (%

)


e) rs=-0.18, p=0.05

0

70

140

210

40 50 60 70


f) rs=-0.50, p<0.01

45

Figure 11: Significant correlations between cover of the abundant taxa Carex stans and a) mean

summer air temperature, b) total summer precipitation, c) the sum of thawing degree days

(TDD), d) mean summer thaw depth and e) mean summer soil temperature during years 2010,

2012, 2013 and 2014 at the site. Rs values represent correlation coefficients from Spearman Rank

correlations. The sample size was 22 plots for each year.

0

50

100

150

2 3.5 5 6.5

Co

ve

r (%

)


rs=0.42, p<0.01 a)

0

50

100

150

6 12 18 24

Precipitation (cm)

b) rs=0.22, p=0.04

0

50

100

150

150 250 350 450

Co

ve

r (%

)

TDD (sum)

c) rs=0.42, p<0.01

0

50

100

150

33 36 39 42

Thaw Depth (cm)

d) rs=0.41, p<0.01

0

50

100

150

2 3 4 5

Co

ve

r (%

)


e) rs=0.57, p<0.01

46

Figure 12: Significant correlations between cover of the abundant taxa Dupontia fisheri and a)

mean summer air temperature, b) the sum of thawing degree days (TDD) and c) mean summer

soil temperature during years 2010, 2012, 2013 and 2014 at the site. Rs values represent

correlation coefficients from Spearman Rank correlations. The sample size was 17 plots for each

year.

0

30

60

90

2.5 4 5.5 7

Co

ve

r (%

)


rs=0.25, p=0.05 a)

0

30

60

90

180 260 340 420

TDD (sum)

b) rs=0.25, p=0.05

0

30

60

90

1.8 2.8 3.8 4.8

Co

ve

r (%

)


rs=0.24; p=0.05 c)

47

Figure 13: Significant correlation between height of graminoids and mean summer soil moisture

during years 2010, 2012, 2013 and 2014 at the site. Height was determined using the maximum

height per plot. Rs value represents correlation coefficient from a Spearman Rank correlation.


0

10

20

30

40 50 60 70

He

igh

t (c

m)


rs=-0.23, p=0.01

48


f) litter and g) standing dead within four generalized plant communities (dry, dry-moist, moist

and wet) between 2010 and 2013 within the Arctic Systems Science grid. The grid was

comprised of 98 vegetation plots which both included and extended beyond the 30 vegetation

plots at the site which were used for the prior analyses. Cover changes were analyzed using a 2-

way ANOVA. ‡ = square root transformed. Lack of comparisons indicate functional group was

found in <5 plots and was subsequently excluded from analysis. Error bars show standard error

of the mean. The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for moist plots and

27 for wet plots. Note change in scale between the functional groups.

0

20

40

60

Dry Dry-moist Moist Wet

Community Type

f) litter‡

0

10

20

30

Cover

(%)

c) forbs‡

0

5

10

15

2010

2013

b) deciduous shrubs‡

0

25

50

75


Cover

(%)

Community Type

g) standing dead‡

0

15

30

45

Cover

(%)

e) lichens‡

0

25

50

75

Cover

(%)

a) bryophytes‡

0

40

80

120

d) graminoids‡

49

Figure 15: Mean cover of a) acrocarpous mosses, b) pleurocarpous mosses, c) Carex stans, d)

Dupontia fisheri and e) Eriophorum russeolum within four generalized plant communities (dry,

dry-moist, moist and wet) between 2010 and 2013 within the Arctic Systems Science grid. The

grid was comprised of 98 vegetation plots which both included and extended beyond the 30


using a 2-way ANOVA. ‡ = square root transformed; † = log transformed. Error bars show

standard error of the mean. The sample size was 24 for dry plots, 26 for dry-moist plots, 21 for

moist plots and 27 for wet plots. Note change in scale between the abundant taxa.

0

12

24

36

Cover

(%)

a) acrocarpous mosses‡

0

25

50

75

Cover

(%)

c) Carex stans‡

0

15

30

45

2010

2013

b) pleurocarpous mosses‡

0

12

24

36


Community Type

d) Dupontia fisheri‡

0

10

20

30


Cover

(%)

Community Type

e) Eriophorum russeolum†

50

Discussion

Due to the large differences observed between years with many of the vegetation metrics,

caution should be exercised when utilizing the traditional method of documenting long-term

vegetation changes over a coarse time series. Results from this study showed that the magnitude

of vegetation changes at the site were dramatic over short time spans of just one to three years.

For example, the cover of functional groups was shifting dramatically at the site, especially

between years 2010 and 2013 when graminoids and forbs more than doubled in cover which was

followed by nearly a halving of the cover of both functional groups just one year later from 2013

to 2014. In addition, even the cover of individual species changed drastically between years as

Carex stans showed considerable increases in cover from years 2010 to 2013 in both the species

within functional groups and the abundant taxa analyses. Likewise, plant height increased

substantially over a two year time span as the height of graminoids increased by over three

centimeters. Thus, by not considering intermediate years, a considerable amount of change of

vegetation metrics may be missed likely as a function of the variability in weather between years.

As such, conclusions reached over a coarse time series may be different than conclusions

reached when interannual variation is considered. Therefore, long-term consecutive time series

are needed when documenting vegetation changes to account for such variability. Other research

studies also note the importance of considering interannual variability when investigating plant

phenology (Iler et al. 2013) and net primary productivity (Knapp & Smith 2001).

To ensure the magnitude of observed vegetation changes were not the result of different

researchers collecting data from year to year, additional analyses were conducted but not

presented here. A simplified version of the point frame method where only top and bottom

encounters are considered was validated by May & Hollister (2012), serving as a reasonably

51

accurate alternative to using all vegetation encounters. This method was applied to all vegetation

cover change over time analyses presented in this study and lesser but still significant changes

were found similar to the results presented in this study. Since the top and bottom method

includes only two vegetation encounters instead of multiple encounters per point, the likelihood

of sampling differences between researchers was reduced considerably. Furthermore, changes in

the height of functional groups over time were also analyzed with average height instead of

maximum height per plot. Again, changes were less pronounced but consistent with the results

presented here. Since changes observed with the different point frame methods and different

vegetation height methods were similar, results presented in this study are unlikely to be the

consequence of different researchers collecting data differently from year to year.

Regardless of the short time span of this study and the major differences in vegetation

metrics between individual years, there was still a significant negative correlation between year

and the cover of standing dead. Dead plant material has been shown to be accumulating in arctic

regions over time (Elmendorf et al. 2012b) but this study shows an opposite pattern which was

likely driven by the high standing dead cover documented in the year 2012 and dramatic declines

observed in later years. The cover of standing dead often varies interannually as it is typically a

function of how long plants retain dead leaves (Knapp & Seastedt 1986) and the large amount of

change detected in this study may be a function of that retention time. Lemming herbivory may

have also the influenced the initial cover of standing dead observed in the year 2010 as Villarreal

et al. (2012) noted that lemming abundances were high during the summers of years 2007-2008

near the study site and significantly altered vegetation community composition in the region

from 2007-2010.

52

The analyses in this study were limited due to sample size and non-consecutive time

series sampling at the site and across the grid. Issues with sample size were apparent as only five

taxa were found on at least half of the site plots and at least half of each of the grid community

types. These five taxa included only mosses and graminoids which comprise merely a portion of

the species composition at the site. Likewise, the site was not sampled in 2011 nor was the grid

sampled in 2011, 2012 or 2014. Thus, only four time points were available for correlations at the

site and only two time points were available for analyzing changes across the grid. Increased

sample size and a longer consecutive time series would be beneficial to help understand observed

changes in vegetation metrics over time.

Similarly, it was not determined whether the changes in cover were the result of changes

in growth, shifts in abundance or a combination of both. For example, these factors were

investigated for vascular growth forms and species at a study site in Atqasuk, Alaska which

showed that changes in the cover of graminoids were explained mainly by individual plant

growth, while changes in the cover of shrubs typically involved both plant growth and density

(Gregory 2014). Similar measures outlined in that study could be applied to the Barrow site to

better understand the mechanisms behind cover changes observed at the site.

Despite the short-term nature of this study, vegetation metrics were still significantly and

the most strongly correlated with different abiotic variables. Other studies have linked soil

temperature with the biomass of various functional groups and species in tundra regions (Hill &

Henry 2011; Natali et al. 2012) and results from this study show similar findings especially as

soil temperature was the most strongly correlated with the cover of forbs, graminoids, litter and

Carex stans. In addition, the impact of of climate warming on the cover of graminoids, litter and

standing dead (Walker et al. 2006; Elmendorf et al. 2012a) as well as Carex stans and Dupontia

53

fisheri (Hollister et al. 2005a; Hollister et al. 2015) is well documented and similar results were

presented in this study. Likewise, precipitation and soil moisture are associated with vegetation

growth in tundra regions (Chapin et al. 1996; Blok et al. 2011) and results from this study mirror

those studies as both abiotic factors were correlated significantly and the most strongly with the

cover and height of certain functional groups. Finally, thaw depth is often correlated with

vegetation growth and community composition in arctic regions (Schuur et al. 2007; Lantz et al.

2009) which was further observed in this study. Therefore, multiple abiotic factors should be

considered when documenting vegetation changes in arctic regions.

Future work should involve identifying other factors, especially biotic ones that may

correlate with vegetation metrics since not all of the vegetation metrics were correlated

significantly with any of the measured abiotic variables. This is especially critical considering

abiotic variables in this study were correlated independently with vegetation metrics as the

variables were inherently related to each other. Leaf length or leaf area index of vascular plant

species would be potential metrics to consider, since they impact point frame sampling and are

related to greening trends in the Arctic (Zhou et al. 2001; Jia et al. 2003). Since each year of

sampling in this study was considered a separate independent time point, winter and spring

processes should also be included such as the timing of snowmelt, soil temperature and microbial

activity which are known to be associated with vegetation composition in arctic regions (Sturm

et al. 2005; Wahren et al. 2005; Aerts et al. 2006). Finally, quantifying herbivory at the sites to

identify the direct impacts of herbivores such as caribou or lemmings on vegetation would also

be useful. Impacts of multiple variables together (both measured in this study and proposed for

future research), perhaps in a multiple regression analyses, could help illuminate observed

vegetation changes.

54

Studies conducted in Barrow identifying vegetation changes within only one or two

vegetation community types was representative of changes across the landscape as the vegetation

communities in this study responded similarly over time. Although the community types

identified in this study were significantly different from each other in years 2010 and 2013, there

was still a considerable amount of overlap between species which blended together likely

explaining the non-significant interactions between community type and year. In addition, results

presented here may be due to using a land cover classification map which was developed with

satellite imagery from 2002. Several times the land cover classifications assigned to the plots did

not match field observations during the years of this study and required modification. A new land

cover classification map of the area which contains imagery obtained during the study period

outlined here is still being finalized elsewhere but would be more representative of the study area

than the map derived from older satellite imagery. Thus, identifying vegetation changes within

one or two vegetation community types serves as a reasonably accurate representation of the

Barrow landscape. However, it is important to note that these results are site specific and results

could be different at other sites across a different range of abiotic conditions.

55

Conclusion

Documenting vegetation changes are critical for numerous reasons. Individual plant

species control energy transfer between trophic levels which is especially critical for herbivore

forage quantity and quality. Functional groups are useful indicators of climate warming, help

simplify vegetation models and maps and have predictive power. The vegetation metrics of cover

and height influence competitive interactions, albedo and habitat suitability while diversity is

linked to the productivity, stability and function of ecosystems. Identifying changes in arctic

regions is especially important considering the numerous consequences associated with current

and future climate warming. Research programs and funding initiatives have been implemented

to document long-term changes in arctic regions such as ARCSS, ITEX and AON. Specifically

the Barrow region, where this study took place, has a rich history of facilitating scientific

research.

However, regardless of the many studies already conducted on vegetation changes in the

Barrow region, knowledge gaps remain. The full consequences of changes in vegetation cover,

height and diversity remain poorly understood but could have significant implications for

ecosystem processes and ecosystem function, especially with future climate warming in arctic

regions. Furthermore, most of the recent studies in the Barrow region as part of ITEX only

utilized one or two vegetation community types (dry heath, wet meadow tundra or both) while

numerous vegetation communities exist across the landscape and are known to function

differently. Therefore, new studies are needed to ensure that changes identified within those

recent studies accurately represent changes occurring across the landscape. This study addressed

those knowledge gaps by investigating how vegetation cover, height and diversity changed over

56

time, correlating observed changes with various abiotic factors, and identifying vegetation

changes within generalized community types across the landscape.

Results from this study showed that the cover of all functional groups and many species

within function groups changed significantly over time indicating that a considerable amount of

change was occurring at the site even with the short duration of the study. This was further

evident by the magnitude of changes observed across the site. The cover of certain functional

groups such as graminoids, litter, standing dead and Carex stans approximately doubled or

halved between individual years of this study and the height of graminoids increased by over

three centimeters in just two years suggesting that caution should be used when documenting

vegetation changes across coarse time series as variability of vegetation metrics in intermediate

years could be missed. While these changes were large and the time span of the study was short,

results still showed that the cover of standing dead was significantly correlated with year. This

was likely a product of the length of time dead leaves are held by plants and lemming herbivory

at the site. Results of this study also illustrated that vegetation metrics were significantly and the

most strongly correlated with different abiotic factors showing that multiple abiotic factors such

as air temperature, precipitation, thawing degree days, thaw depth, soil temperature and soil

moisture should be considered when documenting vegetation changes. Finally, while the cover

of functional groups and the abundant taxa significantly changed over time and cover differed

between community types, the vegetation communities responded similarly over time likely due

to the considerable amount of overlap in the vegetation composition across the grid. Thus, recent

studies utilizing one or two community types to identify vegetation changes were representative

of changes across the landscape.

57

Continued research and monitoring at the site and across the grid is needed. More

vegetation metrics should be considered, especially biotic factors such as leaf length and

herbivory while analyses should include multiple variables to identify how several factors are

influencing vegetation metrics simultaneously rather than on an individual basis as was done

with this study. Further research into the mechanisms influencing vegetation cover, such as

growth, abundance or a combination of both would also be beneficial. Finally, a longer

consecutive time series is needed to identify and understand vegetation changes at the site, the

relationship between vegetation metrics and abiotic factors as well as changes within multiple

vegetation community types over time.

58

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